
# loading the library required for data loading and processing
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib import pyplot as plt
from sklearn.preprocessing import StandardScaler
from sklearn.impute import KNNImputer

#Supress warnings
import warnings
warnings.filterwarnings('ignore')


# read the data using pandas function from 'innercity.csv' file
house_df = pd.read_excel('innercity.xlsx')


# let's check whether data loaded successfully or not, by checking first few records
house_df.head()


# print the number of records and features/aspects we have in the provided file
house_df.shape

(21613, 23)


# let's check out the columns/features we have in the dataset
house_df.columns

Index(['cid', 'dayhours', 'price', 'room_bed', 'room_bath', 'living_measure',
       'lot_measure', 'ceil', 'coast', 'sight', 'condition', 'quality',
       'ceil_measure', 'basement', 'yr_built', 'yr_renovated', 'zipcode',
       'lat', 'long', 'living_measure15', 'lot_measure15', 'furnished',
       'total_area'],
      dtype='object')


# let's see the data types of the features
house_df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 21613 entries, 0 to 21612
Data columns (total 23 columns):
 #   Column            Non-Null Count  Dtype  
---  ------            --------------  -----  
 0   cid               21613 non-null  float64
 1   dayhours          21613 non-null  object 
 2   price             21613 non-null  float64
 3   room_bed          21613 non-null  float64
 4   room_bath         21613 non-null  float64
 5   living_measure    21613 non-null  float64
 6   lot_measure       21613 non-null  float64
 7   ceil              21613 non-null  object 
 8   coast             21613 non-null  float64
 9   sight             21613 non-null  float64
 10  condition         21613 non-null  float64
 11  quality           21612 non-null  float64
 12  ceil_measure      21612 non-null  float64
 13  basement          21612 non-null  float64
 14  yr_built          21612 non-null  object 
 15  yr_renovated      21613 non-null  float64
 16  zipcode           21613 non-null  float64
 17  lat               21613 non-null  float64
 18  long              21613 non-null  object 
 19  living_measure15  21447 non-null  float64
 20  lot_measure15     21584 non-null  float64
 21  furnished         21584 non-null  float64
 22  total_area        21584 non-null  object 
dtypes: float64(18), object(5)
memory usage: 3.8+ MB


# let's check whether our dataset have any null/missing values
house_df.isnull().sum()

cid                 0
dayhours            0
price               0
room_bed            0
room_bath           0
living_measure      0
lot_measure         0
ceil                0
coast               0
sight               0
condition           0
quality             0
ceil_measure        0
basement            0
yr_built            0
yr_renovated        0
zipcode             0
lat                 0
long                0
living_measure15    0
lot_measure15       0
furnished           0
total_area          0
dtype: int64


house_df.long.value_counts()

-122.290    150
-122.300    111
-122.362    104
-122.291    100
-122.372     99
           ... 
-122.457      1
-122.474      1
-121.750      1
-121.893      1
-121.947      1
Name: long, Length: 752, dtype: int64


# Treatment of missing values
house_df['room_bed'] = house_df['room_bed'].fillna(house_df['room_bed'].mode()[0])


house_df['room_bath'] = house_df['room_bath'].fillna(house_df['room_bath'].mode()[0])


house_df['living_measure'] = house_df['living_measure'].fillna(house_df['living_measure'].mode()[0])


house_df['lot_measure'] = house_df['lot_measure'].fillna(house_df['lot_measure'].mode()[0])


house_df['ceil'] = house_df['ceil'].fillna(house_df['ceil'].mode()[0])


house_df['sight'] = house_df['sight'].fillna(house_df['sight'].mode()[0])


house_df['condition'] = house_df['condition'].fillna(house_df['condition'].mode()[0])


house_df['quality'] = house_df['quality'].fillna(house_df['quality'].mode()[0])


house_df['furnished'] = house_df['furnished'].fillna(house_df['furnished'].mode()[0])


house_df['long'] = house_df['long'].replace('$',-122.290)


house_df['total_area'] = house_df['total_area'].fillna(house_df['living_measure'] + house_df['living_measure'])


import plotly.express as px
import pandas as pd
fig = px.scatter_geo(house_df,lat='lat',lon='long', )
fig.update_layout(title = 'lat long, 2014-2015', title_x=0.5)
fig.show()


# let's check whether there's any duplicate record in our dataset or not. If present, we have to remove them
house_df.duplicated().sum()

0


# let's do the 5 - factor analysis of the features
house_df.describe().transpose()


house_df.iloc[:, 2:].columns

Index(['price', 'room_bed', 'room_bath', 'living_measure', 'lot_measure',
       'ceil', 'coast', 'sight', 'condition', 'quality', 'ceil_measure',
       'basement', 'yr_built', 'yr_renovated', 'zipcode', 'lat', 'long',
       'living_measure15', 'lot_measure15', 'furnished', 'total_area'],
      dtype='object')


for col in house_df.iloc[:, 2:].columns:
    print(col)
    print('Skew : ', np.round(house_df[col].skew(),3))
    plt.figure(figsize=(25,6))
    plt.subplot(1,2,1)
    sns.distplot(house_df[col])
    plt.subplot(1,2,2)
    sns.boxplot(house_df[col])
    plt.show()

price
Skew :  4.022

room_bed
Skew :  1.989

room_bath
Skew :  0.505

living_measure
Skew :  1.473

lot_measure
Skew :  13.084

ceil
Skew :  0.622

coast
Skew :  11.457

sight
Skew :  3.401

condition
Skew :  1.039

quality
Skew :  0.771


#cid 
cid_count=house_df.cid.value_counts()
cid_count[cid_count>1].shape

(176,)


#we will create new data frame that can be used for modeling
#We will convert the dayhours to 'month_year' as sale month-year is relevant for analysis

house_dfr=house_df.copy()
house_df.dayhours=house_df.dayhours.str.replace('T000000', "")
house_df.dayhours=pd.to_datetime(house_df.dayhours,format='%Y%m%d')
house_df['month_year']=house_df['dayhours'].apply(lambda x: x.strftime('%B-%Y'))
house_df['month_year'].head()

0       April-2015
1       March-2015
2      August-2014
3     October-2014
4    February-2015
Name: month_year, dtype: object


house_df['month_year'].value_counts()

April-2015        2231
July-2014         2211
June-2014         2180
August-2014       1940
October-2014      1878
March-2015        1875
September-2014    1774
May-2014          1768
December-2014     1471
November-2014     1411
February-2015     1250
January-2015       978
May-2015           646
Name: month_year, dtype: int64


house_df.groupby(['month_year'])['price'].agg('mean')

month_year
April-2015        561933.463021
August-2014       536527.039691
December-2014     524602.893270
February-2015     507919.603200
January-2015      525963.251534
July-2014         544892.161013
June-2014         558123.736239
March-2015        544057.683200
May-2014          548166.600113
May-2015          558193.095975
November-2014     522058.861800
October-2014      539127.477636
September-2014    529315.868095
Name: price, dtype: float64


house_df.price.describe()

count    2.161300e+04
mean     5.401822e+05
std      3.673622e+05
min      7.500000e+04
25%      3.219500e+05
50%      4.500000e+05
75%      6.450000e+05
max      7.700000e+06
Name: price, dtype: float64


plt.figure(figsize=(12,5))
sns.distplot(house_df['price'])

<AxesSubplot:xlabel='price', ylabel='Density'>


house_df['room_bed'].value_counts()

3.0     9875
4.0     6854
2.0     2747
5.0     1595
6.0      270
1.0      197
7.0       38
8.0       13
0.0       13
9.0        6
10.0       3
33.0       1
11.0       1
Name: room_bed, dtype: int64


house_df[house_df['room_bed']==33]


plt.figure(figsize=(12,5))
sns.countplot(house_df.room_bed)

<AxesSubplot:xlabel='room_bed', ylabel='count'>


plt.figure(figsize=(12,5))
sns.countplot(house_df.room_bath)
house_df['room_bath'].value_counts().sort_index()

0.00      10
0.50       4
0.75      72
1.00    3829
1.25       9
1.50    1439
1.75    3031
2.00    1917
2.25    2039
2.50    5466
2.75    1178
3.00     750
3.25     588
3.50     726
3.75     155
4.00     135
4.25      78
4.50     100
4.75      23
5.00      21
5.25      13
5.50      10
5.75       4
6.00       6
6.25       2
6.50       2
6.75       2
7.50       1
7.75       1
8.00       2
Name: room_bath, dtype: int64


plt.figure(figsize=(12,5))
print("Skewness is :",house_df.room_bath.skew())
sns.distplot(house_df.room_bath)

Skewness is : 0.5051364190311984

<AxesSubplot:xlabel='room_bath', ylabel='Density'>


#Data is skewed as visible from plot, as its distribution is normal
plt.figure(figsize=(12,5))
print("Skewness is :",house_df.living_measure.skew())
sns.distplot(house_df.living_measure)
house_df.living_measure.describe()

Skewness is : 1.473414902919285

count    21613.000000
mean      2079.247351
std        918.395090
min        290.000000
25%       1420.000000
50%       1910.000000
75%       2550.000000
max      13540.000000
Name: living_measure, dtype: float64


#Let's plot the boxplot for living_measure
plt.figure(figsize=(20,6))
sns.boxplot(house_df.living_measure)

<AxesSubplot:xlabel='living_measure'>


#checking the no. of data points with Living measure greater than 8000
house_df[house_df['living_measure']>8000]


#Data is skewed as visible from plot
plt.figure(figsize=(12,5))
print("Skewness is :",house_df.lot_measure.skew())
sns.boxplot(house_df.lot_measure)
house_df.lot_measure.describe()

Skewness is : 13.084208709303645

count    2.161300e+04
mean     1.508495e+04
std      4.138574e+04
min      5.200000e+02
25%      5.032000e+03
50%      7.606000e+03
75%      1.066000e+04
max      1.651359e+06
Name: lot_measure, dtype: float64


#checking the no. of data points with Lot measure greater than 1250000
house_df[house_df['lot_measure']>1250000]


#let's see the ceil count for all the records
house_df.ceil.value_counts()

1.0    10719
2.0     8210
1.5     1905
3.0      610
2.5      161
3.5        8
Name: ceil, dtype: int64


plt.figure(figsize=(12,5))
sns.countplot('ceil',data=house_df)

<AxesSubplot:xlabel='ceil', ylabel='count'>


#coast - most houses donot have waterfront view, very few are waterfront
house_df.coast.value_counts()

0.0    21452
1.0      161
Name: coast, dtype: int64


#sight - most sights have not been viewed
house_df.sight.value_counts()

0.0    19494
2.0      959
3.0      510
1.0      332
4.0      318
Name: sight, dtype: int64


#condition - Overall most houses are rated as 3 and above for its condition overall
house_df.condition.value_counts()

3.0    14063
4.0     5655
5.0     1694
2.0      171
1.0       30
Name: condition, dtype: int64


#Quality - most properties have quality rating between 6 to 10
house_df.quality.value_counts()
plt.figure(figsize=(12,5))
sns.countplot('quality',data=house_df)

<AxesSubplot:xlabel='quality', ylabel='count'>


#checking the no. of data points with quality rating as 13
house_df[house_df['quality']==13]


#ceil_measure - its highly skewed
print("Skewness is :", house_df.ceil_measure.skew())
plt.figure(figsize=(12,5))
sns.distplot(house_df.ceil_measure)
house_df.ceil_measure.describe()

Skewness is : 1.4468176475402241

count    21613.000000
mean      1788.343960
std        828.090039
min        290.000000
25%       1190.000000
50%       1560.000000
75%       2210.000000
max       9410.000000
Name: ceil_measure, dtype: float64


sns.factorplot(x='ceil',y='ceil_measure',data=house_df, size = 4, aspect = 2)

<seaborn.axisgrid.FacetGrid at 0x2546492cdf0>


#basement_measure
plt.figure(figsize=(12,5))
sns.distplot(house_df.basement)

<AxesSubplot:xlabel='basement', ylabel='Density'>


house_df[house_df.basement==0].shape

(13126, 24)


#houses have zero measure of basement i.e. they donot have basements
#let's plot boxplot for properties which have basements only
house_df_base=house_df[house_df['basement']>0]
plt.figure(figsize=(12,5))
sns.boxplot(house_df_base['basement'])

<AxesSubplot:xlabel='basement'>


#checking the no. of data points with 'basement' greater than 4000
house_df[house_df['basement']>4000]


#Distribution of houses having basement
plt.figure(figsize=(12,5))
sns.distplot(house_df_base.basement)

<AxesSubplot:xlabel='basement', ylabel='Density'>


#house range from new to very old
plt.figure(figsize=(12,5))
sns.distplot(house_df.yr_built)

<AxesSubplot:xlabel='yr_built', ylabel='Density'>


house_df[house_df['yr_renovated']>0].shape

(914, 24)


#yr_renovated - plot of houses which are renovated
house_df_reno=house_df[house_df['yr_renovated']>0]
plt.figure(figsize=(12,5))
sns.distplot(house_df_reno.yr_renovated)

<AxesSubplot:xlabel='yr_renovated', ylabel='Density'>


import plotly.express as px


fig = px.scatter_geo(house_df,lat='lat',lon='long')


plt.figure(figsize=(12,5))
sns.countplot('furnished',data=house_df)
house_df.furnished.value_counts()

0.0    17367
1.0     4246
Name: furnished, dtype: int64


# let's plot all the variables and confirm our above deduction with more confidence
sns.pairplot(house_df, diag_kind = 'kde')

<seaborn.axisgrid.PairGrid at 0x25465fe2c70>


# let's see corelatoin between the different features
house_corr = house_df.corr(method ='pearson')
house_corr


# Plotting heatmap
plt.subplots(figsize =(15, 8)) 
sns.heatmap(house_corr,cmap="YlGnBu",annot=True)

<AxesSubplot:>


#month,year in which house is sold. Price is not influenced by it, though there are outliers and can be easily seen.
house_df['month_year'] = pd.to_datetime(house_df['month_year'], format='%B-%Y')

house_df.sort_values(["month_year"], axis=0,ascending=True, inplace=True) 
house_df["month_year"] = house_df["month_year"].dt.strftime('%B-%Y')

sns.factorplot(x='month_year',y='price',data=house_df, size=4, aspect=2)
plt.xticks(rotation=90)
#groupby
house_df.groupby('month_year')['price'].agg(['mean','median','size'])


#Room_bed - outliers can be seen easily. Mean and median of price increases with number bedrooms/house uptill a point
#and then drops
sns.factorplot(x='room_bed',y='price',data=house_df, size=4, aspect=2)

#groupby
house_df.groupby('room_bed')['price'].agg(['mean','median','size'])


#room_bath - outliers can be seen easily. Overall mean and median price increares with increasing room_bath
sns.factorplot(x='room_bath',y='price',data=house_df,size=4, aspect=2)
plt.xticks(rotation=90)
#groupby
house_df.groupby('room_bath')['price'].agg(['mean','median','size'])


#living_measure - price increases with increase in living measure
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['living_measure'],house_df['price']))
house_df['living_measure'].describe()

AxesSubplot(0.125,0.125;0.775x0.755)

count    21613.000000
mean      2079.247351
std        918.395090
min        290.000000
25%       1420.000000
50%       1910.000000
75%       2550.000000
max      13540.000000
Name: living_measure, dtype: float64


#lot_measure - there seems to be no relation between lot_measure and price
#lot_measure - data value range is very large so breaking it get better view.
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['lot_measure'],house_df['price']))
house_df['lot_measure'].describe()

AxesSubplot(0.125,0.125;0.775x0.755)

count    2.161300e+04
mean     1.508495e+04
std      4.138574e+04
min      5.200000e+02
25%      5.032000e+03
50%      7.606000e+03
75%      1.066000e+04
max      1.651359e+06
Name: lot_measure, dtype: float64


#lot_measure <25000
plt.figure(figsize=(12,5))
x=house_df[house_df['lot_measure']<25000]
print(sns.scatterplot(x['lot_measure'],x['price']))
x['lot_measure'].describe()

AxesSubplot(0.125,0.125;0.775x0.755)

count    19717.000000
mean      7755.018106
std       4247.414149
min        520.000000
25%       5000.000000
50%       7250.000000
75%       9612.000000
max      24969.000000
Name: lot_measure, dtype: float64


#lot_measure >100000 - price increases with increase in living measure
plt.figure(figsize=(12,5))
y=house_df[house_df['lot_measure']<=75000]
print(sns.scatterplot(y['lot_measure'],y['price']))

AxesSubplot(0.125,0.125;0.775x0.755)


#ceil - median price increases initially and then falls
print(sns.factorplot(x='ceil',y='price',data=house_df, size = 4, aspect = 2))
#groupby
house_df.groupby('ceil')['price'].agg(['mean','median','size'])

<seaborn.axisgrid.FacetGrid object at 0x000002540E358B80>


#coast - mean and median of waterfront view is high however such houses are very small in compare to non-waterfront
#Also, living_measure mean and median is greater for waterfront house.
print(sns.factorplot(x='coast',y='price',data=house_df, size = 4, aspect = 2))
#groupby
house_df.groupby('coast')['living_measure','price'].agg(['median','mean'])

<seaborn.axisgrid.FacetGrid object at 0x000002540DF421F0>


#sight - have outliers. The house sighted more have high price (mean and median) and have large living area as well.
print(sns.factorplot(x='sight',y='price',data=house_df, size = 4, aspect = 2))
#groupby
house_df.groupby('sight')['price','living_measure'].agg(['mean','median','size'])

<seaborn.axisgrid.FacetGrid object at 0x000002540E81C760>


#Sight - Viewed in relation with price and living_measure
#Costlier houses with large living area are sighted more.
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['living_measure'],house_df['price'],hue=house_df['sight'],palette='Paired',legend='full'))

AxesSubplot(0.125,0.125;0.775x0.755)


#condition - as the condition rating increases its price and living measure mean and median also increases.
print(sns.factorplot(x='condition',y='price',data=house_df, size = 4, aspect = 2))
#groupby
house_df.groupby('condition')['price','living_measure'].agg(['mean','median','size'])

<seaborn.axisgrid.FacetGrid object at 0x00000254100303D0>


#Condition - Viewed in relation with price and living_measure. Most houses are rated as 3 or more. 
#We can see some outliers as well
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['living_measure'],house_df['price'],hue=house_df['condition'],palette='Paired',legend='full'))

AxesSubplot(0.125,0.125;0.775x0.755)


#quality - with grade increase price and living_measure increase (mean and median)

print(sns.factorplot(x='quality',y='price',data=house_df, size = 4, aspect = 2))
#groupby
house_df.groupby('quality')['price','living_measure'].agg(['mean','median','size'])

<seaborn.axisgrid.FacetGrid object at 0x000002540E885B50>


#quality - Viewed in relation with price and living_measure. Most houses are graded as 6 or more. 
#We can see some outliers as well
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['living_measure'],house_df['price'],hue=house_df['quality'],palette='coolwarm_r',
                      legend='full'))

AxesSubplot(0.125,0.125;0.775x0.755)


#ceil_measure - price increases with increase in ceil measure
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['ceil_measure'],house_df['price']))
house_df['ceil_measure'].describe()

AxesSubplot(0.125,0.125;0.775x0.755)

count    21613.000000
mean      1788.343960
std        828.090039
min        290.000000
25%       1190.000000
50%       1560.000000
75%       2210.000000
max       9410.000000
Name: ceil_measure, dtype: float64


#basement - price increases with increase in ceil measure
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['basement'],house_df['price']))
house_df['basement'].describe()

AxesSubplot(0.125,0.125;0.775x0.755)

count    21613.000000
mean       291.509045
std        442.575043
min          0.000000
25%          0.000000
50%          0.000000
75%        560.000000
max       4820.000000
Name: basement, dtype: float64


#Basement to analyse data
def create_basement_group(series):
    if series == 0:
        return "No"
    elif series > 0:
        return "Yes"   
house_df['has_basement'] = house_df['basement'].apply(create_basement_group)


house_df


#basement - after binning we data shows with basement houses are costlier and have higher 
#living measure (mean & median)
print(sns.factorplot(x='has_basement',y='price',data=house_df, size = 4, aspect = 2))
house_df.groupby('has_basement')['price','living_measure'].agg(['mean','median','size'])

<seaborn.axisgrid.FacetGrid object at 0x000002540EA4C310>


#basement - have higher price & living measure
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['living_measure'],house_df['price'],hue=house_df['has_basement']))

AxesSubplot(0.125,0.125;0.775x0.755)


#yr_built - outliers can be seen easily.
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['yr_built'],house_df['living_measure']))
#groupby
house_df.groupby('yr_built')['price'].agg(['mean','median','size'])

AxesSubplot(0.125,0.125;0.775x0.755)


#HouseLandRatio - Computing new variable as ratio of living_measure/total_area
#Significes - Land used for construction of house
house_df["HouseLandRatio"]=np.round((house_df['living_measure']/house_df['total_area']),2)*100
house_df["HouseLandRatio"].head()

12235    16.0
14791    35.0
1742     17.0
17829    27.0
14810    17.0
Name: HouseLandRatio, dtype: float64


#yr_renovated - 
plt.figure(figsize=(12,5))
x=house_df[house_df['yr_renovated']>0]
print(sns.scatterplot(x['yr_renovated'],x['price']))
#groupby
x.groupby('yr_renovated')['price'].agg(['mean','median','size'])

AxesSubplot(0.125,0.125;0.775x0.755)


#Lets try to group yr_renovated
#Binning Basement to analyse data
def create_renovated_group(series):
    if series == 0:
        return "No"
    elif series > 0:
        return "Yes"
    
house_df['has_renovated'] = house_df['yr_renovated'].apply(create_renovated_group)


#has_renovated - renovated have higher mean and median, however it does not confirm if the prices of house renovated 
#actually increased or not.
#HouseLandRatio - Renovated house utilized more land area for construction of house
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['living_measure'],house_df['price'],hue=house_df['has_renovated']))
#groupby
house_df.groupby(['has_renovated'])['price','HouseLandRatio'].agg(['mean','median','size'])

AxesSubplot(0.125,0.125;0.775x0.755)


#has_renovated - have higher price & living measure
plt.figure(figsize=(12,5))
x=house_df[house_df['yr_built']<2000]
print(sns.scatterplot(x['living_measure'],x['price'],hue=x['has_renovated']))

AxesSubplot(0.125,0.125;0.775x0.755)


#furnished - Furnished has higher price value and has greater living_measure
plt.figure(figsize=(12,5))
print(sns.scatterplot(house_df['living_measure'],house_df['price'],hue=house_df['furnished']))
#groupby
house_df.groupby('furnished')['price','living_measure','HouseLandRatio'].agg(['mean','median','size'])

AxesSubplot(0.125,0.125;0.775x0.755)


def outlier_treatment(datacolumn):
    sorted(datacolumn)
    Q1,Q3 = np.percentile(datacolumn , [25,75])
    IQR = Q3-Q1
    lower_range = Q1-(1.5 * IQR)
    upper_range = Q3+(1.5 * IQR)
    return lower_range,upper_range


lowerbound,upperbound = outlier_treatment(house_df.ceil_measure)
print(lowerbound,upperbound)

-340.0 3740.0


house_df[(house_df.ceil_measure < lowerbound) | (house_df.ceil_measure > upperbound)]


#dropping the record from the dataset
house_df.drop(house_df[ (house_df.ceil_measure > upperbound) | (house_df.ceil_measure < lowerbound) ].index, inplace=True)


house_df.shape

(21002, 27)


#ceil_measure
print("Skewness is :", house_df.ceil_measure.skew())
plt.figure(figsize=(12,5))
sns.distplot(house_df.ceil_measure)
house_df.ceil_measure.describe()

Skewness is : 0.8200768197068193

count    21002.000000
mean      1712.190077
std        696.037662
min        290.000000
25%       1180.000000
50%       1540.000000
75%       2140.000000
max       3740.000000
Name: ceil_measure, dtype: float64


lowerbound_base,upperbound_base = outlier_treatment(house_df.basement)
print(lowerbound_base,upperbound_base)

-855.0 1425.0


house_df[(house_df.basement < lowerbound_base) | (house_df.basement > upperbound_base)]


#dropping the record from the dataset
house_df.drop(house_df[ (house_df.basement > upperbound_base) | (house_df.basement < lowerbound_base) ].index, inplace=True)


#basement_measure
plt.figure(figsize=(12,5))
sns.distplot(house_df.basement)

<AxesSubplot:xlabel='basement', ylabel='Density'>


#Let's see the boxplot now for basement
plt.figure(figsize=(12,5))
sns.boxplot(house_df['basement'])

<AxesSubplot:xlabel='basement'>


lowerbound_lim,upperbound_lim = outlier_treatment(house_df.living_measure)
print(lowerbound_lim,upperbound_lim)

-160.0 4000.0


house_df[(house_df.living_measure < lowerbound_lim) | (house_df.living_measure > upperbound_lim)]


#dropping the record from the dataset
house_df.drop(house_df[ (house_df.living_measure > upperbound_lim) | (house_df.living_measure < lowerbound_lim) ].index, inplace=True)


#let's see the boxplot after dropping the outliers
plt.figure(figsize=(12,9))
sns.boxplot(house_df['living_measure'])

<AxesSubplot:xlabel='living_measure'>


plt.figure(figsize=(12,5))
sns.distplot(house_df.living_measure)

<AxesSubplot:xlabel='living_measure', ylabel='Density'>


# shape of the data after imputing outliers in living_column
house_df.shape

(20416, 27)


lowerbound_lom,upperbound_lom = outlier_treatment(house_df.lot_measure)
print(lowerbound_lom,upperbound_lom)

-2768.5 17947.5


house_df[(house_df.lot_measure < lowerbound_lom) | (house_df.lot_measure > upperbound_lom)]


#dropping the record from the dataset
house_df.drop(house_df[ (house_df.lot_measure > upperbound_lom) | (house_df.lot_measure < lowerbound_lom) ].index, inplace=True)


#let's plot after treating outliers
plt.figure(figsize=(12,5))
sns.boxplot(house_df['lot_measure'])

<AxesSubplot:xlabel='lot_measure'>


house_df.shape

(18292, 27)


#As we know for room_bed = 33 was outlier from our earlier findings, let's see the record and drop it
house_df[house_df['room_bed']==33]


#dropping the record from the dataset
house_df.drop(house_df[ (house_df.room_bed == 33) ].index, inplace=True)


house_df.shape

(18291, 27)

	count	mean	std	min	25%	50%	75%	max
cid	21613.0	4.580302e+09	2.876566e+09	1.000102e+06	2.123049e+09	3.904930e+09	7.308900e+09	9.900000e+09
price	21613.0	5.401822e+05	3.673622e+05	7.500000e+04	3.219500e+05	4.500000e+05	6.450000e+05	7.700000e+06
room_bed	21613.0	3.369500e+00	9.283307e-01	0.000000e+00	3.000000e+00	3.000000e+00	4.000000e+00	3.300000e+01
room_bath	21613.0	2.117094e+00	7.688002e-01	0.000000e+00	1.750000e+00	2.250000e+00	2.500000e+00	8.000000e+00
living_measure	21613.0	2.079247e+03	9.183951e+02	2.900000e+02	1.420000e+03	1.910000e+03	2.550000e+03	1.354000e+04
lot_measure	21613.0	1.508495e+04	4.138574e+04	5.200000e+02	5.032000e+03	7.606000e+03	1.066000e+04	1.651359e+06
ceil	21613.0	1.492481e+00	5.397630e-01	1.000000e+00	1.000000e+00	1.500000e+00	2.000000e+00	3.500000e+00
coast	21613.0	7.449220e-03	8.598879e-02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00
sight	21613.0	2.337482e-01	7.655206e-01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	4.000000e+00
condition	21613.0	3.407718e+00	6.499332e-01	1.000000e+00	3.000000e+00	3.000000e+00	4.000000e+00	5.000000e+00
quality	21613.0	7.656827e+00	1.175465e+00	1.000000e+00	7.000000e+00	7.000000e+00	8.000000e+00	1.300000e+01
ceil_measure	21613.0	1.788344e+03	8.280900e+02	2.900000e+02	1.190000e+03	1.560000e+03	2.210000e+03	9.410000e+03
basement	21613.0	2.915090e+02	4.425750e+02	0.000000e+00	0.000000e+00	0.000000e+00	5.600000e+02	4.820000e+03
yr_built	21613.0	1.971039e+03	2.938506e+01	1.900000e+03	1.951000e+03	1.975000e+03	1.997000e+03	2.015000e+03
yr_renovated	21613.0	8.440226e+01	4.016792e+02	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	2.015000e+03
zipcode	21613.0	9.807794e+04	5.350503e+01	9.800100e+04	9.803300e+04	9.806500e+04	9.811800e+04	9.819900e+04
lat	21613.0	4.756005e+01	1.385637e-01	4.715590e+01	4.747100e+01	4.757180e+01	4.767800e+01	4.777760e+01
long	21613.0	-1.222140e+02	1.407899e-01	-1.225190e+02	-1.223280e+02	-1.222310e+02	-1.221250e+02	-1.213150e+02
living_measure15	21613.0	1.983632e+03	6.839963e+02	3.990000e+02	1.490000e+03	1.830000e+03	2.360000e+03	6.210000e+03
lot_measure15	21613.0	1.275612e+04	2.727016e+04	6.510000e+02	5.100000e+03	7.620000e+03	1.008000e+04	8.712000e+05
furnished	21613.0	1.964558e-01	3.973264e-01	0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00	1.000000e+00
total_area	21613.0	1.716663e+04	4.156860e+04	1.423000e+03	7.026000e+03	9.562000e+03	1.298400e+04	1.652659e+06

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	...	yr_built	yr_renovated	zipcode	lat	long	living_measure15	lot_measure15	furnished	total_area	month_year
1068	6.762700e+09	2014-10-13	7700000.0	6.0	8.00	12050.0	27600.0	2.5	0.0	3.0	...	1910.0	1987.0	98102.0	47.6298	-122.323	3940.0	8800.0	1.0	39650.0	October-2014
1245	1.924059e+09	2014-06-17	4670000.0	5.0	6.75	9640.0	13068.0	1.0	1.0	4.0	...	1983.0	2009.0	98040.0	47.5570	-122.210	3270.0	10454.0	1.0	22708.0	June-2014
7928	1.225069e+09	2014-05-05	2280000.0	7.0	8.00	13540.0	307752.0	3.0	0.0	4.0	...	1999.0	0.0	98053.0	47.6675	-121.986	4850.0	217800.0	1.0	321292.0	May-2014
10639	9.208900e+09	2014-09-19	6890000.0	6.0	7.75	9890.0	31374.0	2.0	0.0	4.0	...	2001.0	0.0	98039.0	47.6305	-122.290	4540.0	42730.0	1.0	41264.0	September-2014
10718	9.808701e+09	2014-06-11	7060000.0	5.0	4.50	10040.0	37325.0	2.0	1.0	2.0	...	1940.0	2001.0	98004.0	47.6500	-122.214	3930.0	25449.0	1.0	47365.0	June-2014
12794	2.470100e+09	2014-08-04	5570000.0	5.0	5.75	9200.0	35069.0	2.0	0.0	0.0	...	2001.0	0.0	98039.0	47.6289	-122.233	3560.0	24345.0	1.0	44269.0	August-2014
20038	1.247600e+09	2014-10-20	5110000.0	5.0	5.25	8010.0	45517.0	2.0	1.0	4.0	...	1999.0	0.0	98033.0	47.6767	-122.211	3430.0	26788.0	1.0	53527.0	October-2014
20193	2.303900e+09	2014-06-11	2890000.0	5.0	6.25	8670.0	64033.0	2.0	0.0	4.0	...	1965.0	2003.0	98177.0	47.7295	-122.372	4140.0	81021.0	1.0	72703.0	June-2014
20746	6.072800e+09	2014-07-02	3300000.0	5.0	6.25	8020.0	21738.0	2.0	0.0	0.0	...	2001.0	0.0	98006.0	47.5675	-122.189	4160.0	18969.0	1.0	29758.0	July-2014

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	sight	...	yr_built	yr_renovated	zipcode	lat	long	living_measure15	lot_measure15	furnished	total_area	month_year
388	8.532000e+08	2014-07-01	3800000.0	5.0	5.50	7050.0	42840.0	1.0	2.0	...	1978.0	0.0	98004.0	47.6229	-122.220	5070.0	20570.0	1.0	49890.0	July-2014
1068	6.762700e+09	2014-10-13	7700000.0	6.0	8.00	12050.0	27600.0	2.5	3.0	...	1910.0	1987.0	98102.0	47.6298	-122.323	3940.0	8800.0	1.0	39650.0	October-2014
3271	7.237501e+09	2014-10-10	1780000.0	4.0	3.25	4890.0	13402.0	2.0	0.0	...	2004.0	0.0	98059.0	47.5303	-122.131	5790.0	13539.0	1.0	18292.0	October-2014
3649	2.426039e+09	2015-01-30	2420000.0	5.0	4.75	7880.0	24250.0	2.0	2.0	...	1996.0	0.0	98177.0	47.7334	-122.362	2740.0	10761.0	1.0	32130.0	January-2015
4371	1.725059e+09	2014-11-20	2390000.0	4.0	4.00	6330.0	13296.0	2.0	2.0	...	2000.0	0.0	98033.0	47.6488	-122.201	2200.0	9196.0	1.0	19626.0	November-2014
8420	9.831200e+09	2015-03-04	2480000.0	5.0	3.75	6810.0	7500.0	2.5	0.0	...	1922.0	0.0	98102.0	47.6285	-122.322	2660.0	7500.0	1.0	14310.0	March-2015
10639	9.208900e+09	2014-09-19	6890000.0	6.0	7.75	9890.0	31374.0	2.0	4.0	...	2001.0	0.0	98039.0	47.6305	-122.290	4540.0	42730.0	1.0	41264.0	September-2014
10832	4.139900e+09	2015-04-20	2340000.0	4.0	2.50	4500.0	35200.0	1.0	0.0	...	1988.0	0.0	98006.0	47.5477	-122.126	4760.0	35200.0	1.0	39700.0	April-2015
11459	1.068000e+09	2014-09-23	3200000.0	6.0	5.00	7100.0	18200.0	2.5	0.0	...	1933.0	2002.0	98199.0	47.6427	-122.408	3130.0	6477.0	1.0	25300.0	September-2014
12794	2.470100e+09	2014-08-04	5570000.0	5.0	5.75	9200.0	35069.0	2.0	0.0	...	2001.0	0.0	98039.0	47.6289	-122.233	3560.0	24345.0	1.0	44269.0	August-2014
16985	2.303900e+09	2014-09-11	3800000.0	3.0	4.25	5510.0	35000.0	2.0	4.0	...	1997.0	0.0	98177.0	47.7296	-122.370	3430.0	45302.0	1.0	40510.0	September-2014
20193	2.303900e+09	2014-06-11	2890000.0	5.0	6.25	8670.0	64033.0	2.0	4.0	...	1965.0	2003.0	98177.0	47.7295	-122.372	4140.0	81021.0	1.0	72703.0	June-2014
20547	3.303850e+09	2014-12-12	2980000.0	5.0	5.50	7400.0	18898.0	2.0	3.0	...	2001.0	0.0	98006.0	47.5431	-122.112	6110.0	26442.0	1.0	26298.0	December-2014

	cid	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	condition	...	basement	yr_built	yr_renovated	zipcode	lat	long	living_measure15	lot_measure15	furnished	total_area
cid	1.000000	-0.016797	0.001492	0.005991	-0.012240	-0.131747	0.019379	-0.004184	0.011776	-0.024273	...	-0.005151	0.021586	-0.016907	-0.008224	-0.001891	0.020888	-0.003687	-0.138713	-0.010099	-0.131665
price	-0.016797	1.000000	0.307766	0.524081	0.701913	0.089681	0.256385	0.266042	0.396999	0.035556	...	0.323837	0.053541	0.126442	-0.053168	0.306919	0.021199	0.583086	0.082604	0.565834	0.104858
room_bed	0.001492	0.307766	1.000000	0.514256	0.575828	0.032011	0.174115	-0.006080	0.078215	0.027836	...	0.301896	0.153670	0.018437	-0.152556	-0.009410	0.129406	0.389903	0.029401	0.258431	0.044477
room_bath	0.005991	0.524081	0.514256	1.000000	0.752058	0.087566	0.496180	0.061872	0.185854	-0.126503	...	0.282460	0.504725	0.050342	-0.203312	0.024818	0.221594	0.563913	0.086683	0.483371	0.103792
living_measure	-0.012240	0.701913	0.575828	0.752058	1.000000	0.172689	0.352734	0.102560	0.284288	-0.059032	...	0.434640	0.317159	0.055154	-0.199577	0.052659	0.239967	0.752584	0.183499	0.632137	0.194146
lot_measure	-0.131747	0.089681	0.032011	0.087566	0.172689	1.000000	-0.005297	0.021143	0.074968	-0.008531	...	0.015358	0.052734	0.007639	-0.129231	-0.085902	0.228888	0.144837	0.715986	0.119182	0.998042
ceil	0.019379	0.256385	0.174115	0.496180	0.352734	-0.005297	1.000000	0.024136	0.028945	-0.262111	...	-0.244864	0.486468	0.005351	-0.058419	0.050372	0.125028	0.277406	-0.011386	0.347236	0.002384
coast	-0.004184	0.266042	-0.006080	0.061872	0.102560	0.021143	0.024136	1.000000	0.400220	0.017683	...	0.079239	-0.025917	0.088358	0.030289	-0.014139	-0.041954	0.086388	0.030738	0.069571	0.023399
sight	0.011776	0.396999	0.078215	0.185854	0.284288	0.074968	0.028945	0.400220	1.000000	0.046519	...	0.277243	-0.052804	0.102980	0.084386	0.006001	-0.078190	0.281213	0.073016	0.219898	0.080993
condition	-0.024273	0.035556	0.027836	-0.126503	-0.059032	-0.008531	-0.262111	0.017683	0.046519	1.000000	...	0.173736	-0.360446	-0.060494	0.003040	-0.015471	-0.106690	-0.091782	-0.002926	-0.122410	-0.009714
quality	0.008159	0.667482	0.356037	0.663313	0.762179	0.113851	0.457346	0.082515	0.250867	-0.144749	...	0.168417	0.446192	0.014422	-0.184827	0.114127	0.198019	0.710084	0.119296	0.788124	0.129891
ceil_measure	-0.010801	0.605599	0.476983	0.683493	0.876075	0.183409	0.522225	0.071331	0.167396	-0.158318	...	-0.051906	0.422937	0.023297	-0.261141	-0.000753	0.343393	0.728684	0.194179	0.652272	0.202091
basement	-0.005151	0.323837	0.301896	0.282460	0.434640	0.015358	-0.244864	0.079239	0.277243	0.173736	...	1.000000	-0.133209	0.071323	0.074845	0.110538	-0.144688	0.199080	0.017504	0.092516	0.024954
yr_built	0.021586	0.053541	0.153670	0.504725	0.317159	0.052734	0.486468	-0.025917	-0.052804	-0.360446	...	-0.133209	1.000000	-0.223782	-0.346118	-0.148182	0.408205	0.323961	0.070622	0.304536	0.059583
yr_renovated	-0.016907	0.126442	0.018437	0.050342	0.055154	0.007639	0.005351	0.088358	0.102980	-0.060494	...	0.071323	-0.223782	1.000000	0.064357	0.029398	-0.068209	-0.002490	0.007960	0.017342	0.008919
zipcode	-0.008224	-0.053168	-0.152556	-0.203312	-0.199577	-0.129231	-0.058419	0.030289	0.084386	0.003040	...	0.074845	-0.346118	0.064357	1.000000	0.267048	-0.563487	-0.278345	-0.147449	-0.138776	-0.133548
lat	-0.001891	0.306919	-0.009410	0.024818	0.052659	-0.085902	0.050372	-0.014139	0.006001	-0.015471	...	0.110538	-0.148182	0.029398	0.267048	1.000000	-0.135392	0.047997	-0.085843	0.080940	-0.083543
long	0.020888	0.021199	0.129406	0.221594	0.239967	0.228888	0.125028	-0.041954	-0.078190	-0.106690	...	-0.144688	0.408205	-0.068209	-0.563487	-0.135392	1.000000	0.331886	0.254482	0.187309	0.233804
living_measure15	-0.003687	0.583086	0.389903	0.563913	0.752584	0.144837	0.277406	0.086388	0.281213	-0.091782	...	0.199080	0.323961	-0.002490	-0.278345	0.047997	0.331886	1.000000	0.183929	0.618182	0.161131
lot_measure15	-0.138713	0.082604	0.029401	0.086683	0.183499	0.715986	-0.011386	0.030738	0.073016	-0.002926	...	0.017504	0.070622	0.007960	-0.147449	-0.085843	0.254482	0.183929	1.000000	0.129849	0.719254
furnished	-0.010099	0.565834	0.258431	0.483371	0.632137	0.119182	0.347236	0.069571	0.219898	-0.122410	...	0.092516	0.304536	0.017342	-0.138776	0.080940	0.187309	0.618182	0.129849	1.000000	0.132590
total_area	-0.131665	0.104858	0.044477	0.103792	0.194146	0.998042	0.002384	0.023399	0.080993	-0.009714	...	0.024954	0.059583	0.008919	-0.133548	-0.083543	0.233804	0.161131	0.719254	0.132590	1.000000

	mean	median	size
room_bath
0.00	4.490950e+05	317500.0	10
0.50	2.373750e+05	264000.0	4
0.75	2.945209e+05	273500.0	72
1.00	3.470889e+05	320000.0	3829
1.25	6.217722e+05	516500.0	9
1.50	4.091834e+05	370000.0	1439
1.75	4.549409e+05	422800.0	3031
2.00	4.575184e+05	422500.0	1917
2.25	5.337925e+05	472500.0	2039
2.50	5.527632e+05	499330.0	5466
2.75	6.605538e+05	605000.0	1178
3.00	7.087325e+05	600000.0	750
3.25	9.710299e+05	837352.0	588
3.50	9.329520e+05	823250.0	726
3.75	1.198179e+06	1070000.0	155
4.00	1.271616e+06	1060000.0	135
4.25	1.535072e+06	1390000.0	78
4.50	1.334211e+06	1060000.0	100
4.75	2.022300e+06	2300000.0	23
5.00	1.674167e+06	1430000.0	21
5.25	1.817962e+06	1420000.0	13
5.50	2.522500e+06	2340000.0	10
5.75	2.492500e+06	1930000.0	4
6.00	2.948333e+06	2895000.0	6
6.25	3.095000e+06	3095000.0	2
6.50	1.710000e+06	1710000.0	2
6.75	2.735000e+06	2735000.0	2
7.50	4.500000e+05	450000.0	1
7.75	6.890000e+06	6890000.0	1
8.00	4.990000e+06	4990000.0	2

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	...	basement	yr_built	zipcode	lat	long	living_measure15	lot_measure15	total_area
0	3.876101e+09	20150427T000000	600000.0	4.0	1.75	3050.0	9440.0	1.0	0.0	0.0	...	1250.0	1966.0	98034.0	47.7228	-122.183	2020.0	8660.0	12490.0
1	3.145600e+09	20150317T000000	190000.0	2.0	1.00	670.0	3101.0	1.0	0.0	0.0	...	0.0	1948.0	98118.0	47.5546	-122.274	1660.0	4100.0	3771.0
2	7.129303e+09	20140820T000000	735000.0	4.0	2.75	3040.0	2415.0	2.0	1.0	4.0	...	0.0	1966.0	98118.0	47.5188	-122.256	2620.0	2433.0	5455.0
3	7.338220e+09	20141010T000000	257000.0	3.0	2.50	1740.0	3721.0	2.0	0.0	0.0	...	0.0	2009.0	98002.0	47.3363	-122.213	2030.0	3794.0	5461.0
4	7.950301e+09	20150218T000000	450000.0	2.0	1.00	1120.0	4590.0	1.0	0.0	0.0	...	0.0	1924.0	98118.0	47.5663	-122.285	1120.0	5100.0	5710.0

	mean	median	size
month_year
April-2015	561933.463021	476500.0	2231
August-2014	536527.039691	442100.0	1940
December-2014	524602.893270	432500.0	1471
February-2015	507919.603200	425545.0	1250
January-2015	525963.251534	438500.0	978
July-2014	544892.161013	465000.0	2211
June-2014	558123.736239	465000.0	2180
March-2015	544057.683200	450000.0	1875
May-2014	548166.600113	465000.0	1768
May-2015	558193.095975	455000.0	646
November-2014	522058.861800	435000.0	1411
October-2014	539127.477636	446900.0	1878
September-2014	529315.868095	450000.0	1774

	mean	median	size
room_bed
0.0	4.102231e+05	288000.0	13
1.0	3.189286e+05	299000.0	197
2.0	4.013572e+05	373500.0	2747
3.0	4.667563e+05	414500.0	9875
4.0	6.357284e+05	549950.0	6854
5.0	7.867329e+05	619000.0	1595
6.0	8.274895e+05	652500.0	270
7.0	9.514478e+05	728580.0	38
8.0	1.105077e+06	700000.0	13
9.0	8.939998e+05	817000.0	6
10.0	8.200000e+05	660000.0	3
11.0	5.200000e+05	520000.0	1
33.0	6.400000e+05	640000.0	1

	mean	median	size
ceil
1.0	4.427711e+05	390000.0	10719
1.5	5.593744e+05	525000.0	1905
2.0	6.491210e+05	543250.0	8210
2.5	1.061021e+06	799200.0	161
3.0	5.831248e+05	490500.0	610
3.5	9.339375e+05	534500.0	8

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	...	yr_renovated	zipcode	lat	long	living_measure15	lot_measure15	furnished	total_area	month_year	has_basement
12235	1.425059e+09	2014-05-07	460000.0	3.0	2.00	1760.0	9055.0	2.0	0.0	0.0	...	0.0	98052.0	47.6534	-122.128	2010.0	9383.0	0.0	10815.0	May-2014	No
14791	7.942601e+09	2014-05-20	345600.0	5.0	3.50	2800.0	5120.0	2.5	0.0	0.0	...	2005.0	98122.0	47.6059	-122.310	1780.0	5120.0	1.0	7920.0	May-2014	No
1742	5.652600e+09	2014-05-02	750000.0	3.0	1.75	2240.0	10578.0	2.0	0.0	0.0	...	0.0	98115.0	47.6954	-122.292	1570.0	10578.0	0.0	12818.0	May-2014	Yes
17829	3.529200e+09	2014-05-14	325000.0	3.0	2.50	2220.0	6049.0	2.0	0.0	0.0	...	0.0	98031.0	47.3972	-122.182	1980.0	7226.0	0.0	8269.0	May-2014	No
14810	5.631501e+09	2014-05-15	390000.0	3.0	2.50	2240.0	10800.0	2.0	0.0	0.0	...	0.0	98028.0	47.7433	-122.229	1900.0	9900.0	0.0	13040.0	May-2014	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
3495	6.324000e+09	2015-05-11	210000.0	2.0	1.00	990.0	8140.0	1.0	0.0	0.0	...	0.0	98116.0	47.5828	-122.382	2150.0	5000.0	0.0	9130.0	May-2015	No
13781	6.189200e+09	2015-05-04	617450.0	3.0	2.50	1580.0	14398.0	1.0	0.0	0.0	...	0.0	98005.0	47.6328	-122.174	1540.0	14407.0	0.0	15978.0	May-2015	Yes
9980	5.710500e+09	2015-05-06	545000.0	3.0	2.00	1900.0	9975.0	1.0	0.0	0.0	...	0.0	98027.0	47.5307	-122.054	2140.0	9825.0	0.0	11875.0	May-2015	Yes
3462	7.702601e+09	2015-05-05	603000.0	4.0	3.50	3610.0	6345.0	2.0	0.0	0.0	...	0.0	98058.0	47.4283	-122.102	3010.0	29279.0	1.0	9955.0	May-2015	Yes
6682	8.699100e+09	2015-05-05	370000.0	6.0	2.75	3240.0	5750.0	1.0	0.0	0.0	...	0.0	98002.0	47.3054	-122.221	1230.0	5750.0	0.0	8990.0	May-2015	Yes

	mean	median	size
yr_built
1900.0	581536.632184	549000.0	87
1901.0	557108.344828	550000.0	29
1902.0	673192.592593	624000.0	27
1903.0	480958.195652	461000.0	46
1904.0	583867.755556	478000.0	45
...	...	...	...
2011.0	544648.384615	440000.0	130
2012.0	527436.982353	448475.0	170
2013.0	678599.582090	565000.0	201
2014.0	677006.203833	590962.5	574
2015.0	762970.162162	628000.0	37

	mean	median	size
yr_renovated
1934.0	459950.000000	459950.0	1
1940.0	378400.000000	378400.0	2
1944.0	521000.000000	521000.0	1
1945.0	398666.666667	375000.0	3
1946.0	351137.500000	351137.5	2
...	...	...	...
2011.0	607496.153846	577000.0	13
2012.0	625181.818182	515000.0	11
2013.0	664960.810811	560000.0	37
2014.0	655030.098901	575000.0	91
2015.0	659156.250000	651000.0	16

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	...	lat	long	living_measure15	lot_measure15	furnished	total_area	month_year	has_basement	HouseLandRatio	has_renovated
17852	7.237550e+09	2014-05-12	1230000.0	4.0	4.50	5420.0	101930.0	1.0	0.0	0.0	...	47.6561	-122.005	4760.0	101930.0	1.0	107350.0	May-2014	Yes	5.0	No
10641	3.892500e+09	2014-05-21	1550000.0	3.0	2.50	4460.0	26027.0	2.0	0.0	0.0	...	47.6573	-122.173	3770.0	26027.0	1.0	30487.0	May-2014	No	15.0	No
18983	4.250690e+08	2014-05-05	1090000.0	4.0	2.50	4340.0	141570.0	2.5	0.0	0.0	...	47.6805	-122.048	2720.0	97138.0	1.0	145910.0	May-2014	No	3.0	No
10456	7.397300e+09	2014-05-29	2750000.0	4.0	3.25	4430.0	21000.0	2.0	0.0	0.0	...	47.6398	-122.237	3930.0	20000.0	1.0	25430.0	May-2014	No	17.0	Yes
1990	2.616801e+09	2014-05-30	840000.0	7.0	4.50	4290.0	37607.0	1.5	0.0	0.0	...	47.4812	-122.033	2810.0	40510.0	1.0	41897.0	May-2014	No	10.0	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
10744	3.751600e+09	2015-05-08	510000.0	4.0	2.50	4073.0	17334.0	2.0	0.0	0.0	...	47.2949	-122.270	1780.0	9625.0	0.0	21407.0	May-2015	No	19.0	No
11285	2.424059e+09	2015-05-08	2000000.0	4.0	3.25	5640.0	35006.0	2.0	0.0	2.0	...	47.5491	-122.104	4920.0	35033.0	1.0	40646.0	May-2015	Yes	14.0	No
18840	6.065300e+09	2015-05-06	4210000.0	5.0	6.00	7440.0	21540.0	2.0	0.0	0.0	...	47.5692	-122.189	4740.0	19329.0	1.0	28980.0	May-2015	Yes	26.0	No
9276	1.266200e+09	2015-05-06	1850000.0	4.0	3.25	4160.0	10335.0	2.0	0.0	0.0	...	47.6235	-122.192	1840.0	10333.0	1.0	14495.0	May-2015	No	29.0	No
19101	1.623089e+09	2015-05-06	920000.0	4.0	3.75	4030.0	503989.0	2.0	0.0	0.0	...	47.4807	-121.795	2110.0	71874.0	1.0	508019.0	May-2015	No	1.0	No

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	...	lat	long	living_measure15	lot_measure15	furnished	total_area	month_year	has_basement	HouseLandRatio	has_renovated
1724	1.424059e+09	2014-05-16	1270000.0	4.0	3.00	5520.0	8313.0	2.0	0.0	3.0	...	47.5655	-122.129	3770.0	8278.0	1.0	13833.0	May-2014	Yes	40.0	No
1576	5.152100e+09	2014-05-29	472000.0	6.0	2.50	4410.0	14034.0	1.0	0.0	2.0	...	47.3376	-122.324	2600.0	13988.0	1.0	18444.0	May-2014	Yes	24.0	No
20475	9.322800e+09	2014-05-20	879950.0	4.0	2.25	3500.0	13875.0	1.0	0.0	4.0	...	47.5083	-122.388	2960.0	15000.0	1.0	17375.0	May-2014	Yes	20.0	No
4347	3.623500e+09	2014-05-13	2450000.0	4.0	4.50	5030.0	11023.0	2.0	0.0	2.0	...	47.5722	-122.236	3640.0	11490.0	1.0	16053.0	May-2014	Yes	31.0	No
4400	7.784400e+09	2014-05-05	497300.0	6.0	2.75	3200.0	9200.0	1.0	0.0	2.0	...	47.4920	-122.364	2220.0	9500.0	0.0	12400.0	May-2014	Yes	26.0	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2506	7.964410e+09	2015-05-04	700000.0	4.0	3.50	5360.0	25800.0	1.0	0.0	0.0	...	47.6099	-122.054	2650.0	21781.0	1.0	31160.0	May-2015	Yes	17.0	No
4016	1.742800e+09	2015-05-04	463828.0	5.0	1.75	3250.0	13702.0	1.0	0.0	2.0	...	47.4883	-122.225	2620.0	11328.0	0.0	16952.0	May-2015	Yes	19.0	No
9621	1.822079e+09	2015-05-04	500000.0	3.0	2.00	3040.0	41072.0	1.0	0.0	0.0	...	47.3944	-121.972	2230.0	54014.0	0.0	44112.0	May-2015	Yes	7.0	No
4248	1.925069e+09	2015-05-11	2200000.0	5.0	4.25	4640.0	22703.0	2.0	1.0	4.0	...	47.6393	-122.097	3140.0	14200.0	0.0	27343.0	May-2015	Yes	17.0	No
3365	2.524049e+09	2015-05-12	1380000.0	5.0	4.25	4050.0	18827.0	1.0	0.0	2.0	...	47.5323	-122.237	3600.0	25120.0	1.0	22877.0	May-2015	Yes	18.0	No

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	...	lat	long	living_measure15	lot_measure15	furnished	total_area	month_year	has_basement	HouseLandRatio	has_renovated
14696	7.230001e+08	2014-05-05	1400000.0	5.0	3.50	4010.0	8510.0	2.0	0.0	1.0	...	47.6578	-122.286	2610.0	6128.0	1.0	12520.0	May-2014	Yes	32.0	No
9776	2.516201e+08	2014-05-30	2400000.0	4.0	3.25	4140.0	20734.0	1.0	0.0	1.0	...	47.6344	-122.215	4020.0	20008.0	1.0	24874.0	May-2014	Yes	17.0	Yes
15200	1.338600e+09	2014-05-28	1970000.0	8.0	3.50	4440.0	6480.0	2.0	0.0	3.0	...	47.6310	-122.303	4440.0	8640.0	1.0	10920.0	May-2014	Yes	41.0	No
2693	8.562710e+09	2014-05-05	890000.0	4.0	4.25	4420.0	5750.0	2.0	0.0	0.0	...	47.5404	-122.073	4420.0	5750.0	1.0	10170.0	May-2014	Yes	43.0	No
13385	3.758900e+09	2014-05-07	1530000.0	5.0	4.50	4270.0	8076.0	2.0	0.0	0.0	...	47.6990	-122.206	4100.0	10631.0	1.0	12346.0	May-2014	Yes	35.0	No
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
18375	8.562720e+09	2015-04-30	1350000.0	4.0	3.50	4740.0	8611.0	2.0	0.0	3.0	...	47.5375	-122.070	4042.0	8321.0	1.0	13351.0	April-2015	Yes	36.0	No
8258	2.421059e+09	2015-05-11	640000.0	4.0	2.50	4090.0	215186.0	2.0	0.0	0.0	...	47.2964	-122.116	2430.0	142005.0	0.0	219276.0	May-2015	Yes	2.0	No
9506	1.525069e+09	2015-05-04	442500.0	5.0	3.25	4240.0	226097.0	2.0	0.0	0.0	...	47.6472	-122.017	2980.0	217800.0	0.0	230337.0	May-2015	Yes	2.0	No
14071	5.428000e+09	2015-05-11	770000.0	5.0	3.50	4750.0	8234.0	2.0	0.0	2.0	...	47.3574	-122.318	2160.0	14496.0	1.0	12984.0	May-2015	Yes	37.0	No
9931	1.118000e+09	2015-05-08	3400000.0	4.0	4.00	4260.0	11765.0	2.0	0.0	0.0	...	47.6380	-122.288	4260.0	10408.0	1.0	16025.0	May-2015	Yes	27.0	Yes

	price			living_measure
	mean	median	size	mean	median	size
quality
1.0	1.420000e+05	142000.0	1	290.000000	290.0	1
3.0	2.056667e+05	262000.0	3	596.666667	600.0	3
4.0	2.143810e+05	205000.0	29	660.482759	660.0	29
5.0	2.485240e+05	228700.0	242	983.326446	905.0	242
6.0	3.019166e+05	275276.5	2038	1191.796860	1120.0	2038
7.0	4.025850e+05	375000.0	8982	1689.456134	1630.0	8982
8.0	5.429310e+05	510000.0	6067	2183.522993	2150.0	6067
9.0	7.737382e+05	720000.0	2615	2865.405736	2820.0	2615
10.0	1.072347e+06	914327.0	1134	3520.299824	3450.0	1134
11.0	1.497792e+06	1280000.0	399	4395.448622	4260.0	399
12.0	2.192500e+06	1820000.0	90	5471.588889	4965.0	90
13.0	3.710769e+06	2980000.0	13	7483.076923	7100.0	13

	cid	dayhours	price	room_bed	room_bath	living_measure	lot_measure	ceil	coast	sight	...	lat	long	living_measure15	lot_measure15	furnished	total_area	month_year	has_basement	HouseLandRatio	has_renovated
20407	3.200690e+08	2014-05-14	305000.0	4.0	1.50	1590.0	131551.0	1.0	0.0	3.0	...	47.2558	-122.024	2280.0	108028.0	0.0	133141.0	May-2014	No	1.0	No
10566	2.322069e+09	2014-05-07	630000.0	3.0	2.50	2680.0	327135.0	2.0	0.0	0.0	...	47.3783	-122.003	2020.0	60080.0	0.0	329815.0	May-2014	No	1.0	No
1718	3.323059e+09	2014-05-28	326000.0	3.0	2.75	1720.0	28000.0	1.0	0.0	0.0	...	47.4375	-122.176	2000.0	41817.0	0.0	29720.0	May-2014	No	6.0	No
18962	5.875503e+08	2014-05-02	604000.0	3.0	2.50	3240.0	33151.0	2.0	0.0	2.0	...	47.3256	-122.378	4050.0	24967.0	1.0	36391.0	May-2014	No	9.0	No
20401	1.240690e+08	2014-05-05	600000.0	3.0	1.75	1670.0	39639.0	1.0	0.0	0.0	...	47.5929	-121.989	2330.0	30492.0	0.0	41309.0	May-2014	No	4.0	Yes
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17539	7.932000e+09	2015-05-12	602500.0	2.0	2.50	3090.0	47044.0	1.0	0.0	0.0	...	47.4291	-122.177	1860.0	62829.0	1.0	50134.0	May-2015	Yes	6.0	No
13854	1.324079e+09	2015-05-04	378000.0	3.0	1.50	1050.0	57499.0	1.0	0.0	0.0	...	47.5602	-121.860	1460.0	42688.0	0.0	58549.0	May-2015	No	2.0	No
20733	5.561000e+09	2015-05-05	525000.0	3.0	1.75	2620.0	38350.0	1.0	0.0	0.0	...	47.4619	-121.991	2170.0	36962.0	0.0	40970.0	May-2015	Yes	6.0	No
19141	6.260592e+08	2015-05-06	532500.0	4.0	2.00	2220.0	23750.0	1.0	0.0	0.0	...	47.7759	-122.214	2650.0	21167.0	0.0	25970.0	May-2015	No	9.0	No
13811	4.027701e+09	2015-05-01	480000.0	3.0	1.75	2920.0	21375.0	1.0	0.0	0.0	...	47.7666	-122.265	1540.0	8482.0	0.0	24295.0	May-2015	Yes	12.0	No